Social determinants of health (SDoH) are the factors which lie outside of the traditional health system, such as employment or access to nutritious foods, that influence health outcomes. Some efforts have focused on identifying vulnerable populations during the COVID-19 pandemic, however, both the short- and long-term social impacts of the pandemic on individuals and populations are not well understood. This paper presents a pipeline to discover health outcomes and related social factors based on trending SDoH at population-level using Google Trends. A knowledge graph was built from a corpus of research literature (PubMed) and the social determinants that trended high at the start of the pandemic were examined. This paper reports on related social and health concepts which may be impacted by the COVID-19 outbreak and may be important to monitor as the pandemic evolves. The proposed pipeline should have wider applicability in surfacing related social or clinical characteristics of interest, outbreak surveillance, or to mine relations between social and health concepts that can, in turn, help inform and support citizen-centred services.

There is a growing trend in building deep learning patient representations from health records to obtain a comprehensive view of a patient’s data for machine learning tasks. This paper proposes a reproducible approach to generate patient pathways from health records and to transform them into a machine-processable image-like structure useful for deep learning tasks. Based on this approach, we generated over a million pathways from FAIR synthetic health records and used them to train a convolutional neural network. Our initial experiments show the accuracy of the CNN on a prediction task is comparable or better than other autoencoders trained on the same data, while requiring significantly less computational resources for training. We also assess the impact of the size of the training dataset on autoencoders performances. The source code for generating pathways from health records is provided as open source.

Background Digital sources such as Internet-based search tools have created prospects for augmenting traditional public health surveillance. These can support the identification of emerging public health concerns, intervention evaluation or policy making. Social Determinants of Health(SDoH) are key factors linked to health outcomes yet they are poorly recorded and seldom used. Scientific papers have reported SDoH impacts on various outcomes yet this data has not been explored for population trend analyses. We present an analysis of our approach to combining insights mined from PubMed with online search trends. Methods PubMed-2019 database was used to build a knowledge graph(KG) of connected health and social concepts based on relative co-occurrences found throughout the abstracts. We then observed Google search trends for 10 SDoH concepts at the outset of the 2020 pandemic (March-May) and compared them with the previous 4 years. For concepts with increasing trends, the KG was used to identify other potentially relevant concepts. Subsequently we continued observing online trends for a further 12 months in order to examine the KG concepts' trends. Results Our analysis showed Food Security and Unemployment trended the highest compared to previous years. These became the seed concepts used to traverse the KG, where the top 20 relevant concepts were identified for each seed. An analysis of the 8 concepts overlapping both seeds showed 6 with increasing trends during follow-up. Correlation coefficients were computed and positive relations observed (Unemployment+Anxiety, r=.72; Distress+Food Security, r =.62). Data will be discussed. Conclusions Positive correlations were observed between data from a 2019 PubMed KG and online search trends during COVID-19. These preliminary results suggest value in combining these digital sources to strengthen public health systems. This is important to understand the interactions between health and social factors and identify emerging trends. Key messages Combining data from scientific research papers with online search trends yielded interesting results that may further complement traditional surveillance systems and clinical case ascertainment. Social determinants of health data should play an increasingly important part in complementing public health surveillance systems and in strengthening population trend analyses.

This paper presents the results of a new approach to discover related health and social factors during the COVID-19 pandemic. The approach leverages a knowledge graph of related concepts mined from a corpus of published evidence (PubMed) prior to the pandemic. Population trends from online searches were used to identify social determinants of health (SDoH) concepts that trended high at the outset of the pandemic from a list of SDoH topics from the World Health Organization (WHO). The trending concepts were then mapped to the knowledge graph and a subsequent analysis of the derived insights, spanning two years, was conducted. This paper suggests an approach to derive new related health and social factors that may have either played a role in, or been affected by, the onset of the global COVID-19 pandemic. In particular, our results show how, from a list of SDoH topics, Food Security, Unemployment trended the highest at the start of the pandemic. Further work is needed to continue to ascertain the validity of the derived relations in a population health context and to improve mining insights from published evidence.

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